1. Field of the Invention
The invention relates to dynamic message filtering, such as for example filtering incoming messages in response to their content; in one embodiment, messages can be delivered, or other action taken, in response to a result of dynamic filtering.
2. Related Art
In computer communication networks, it is common to send and receive messages among users, such as for the purpose of correspondence, distributing information, and responding to requests. One method for doing so is electronic mail, also known as email. One problem that has arisen in the art is that some messages are unwanted. Moreover, it has become common for advertisers and other message senders to collect relatively large numbers of email addresses, and to send unsolicited advertising in bulk to recipients at those email addresses. When the number of such unsolicited bulk email messages is relatively large, it can take substantial time and effort for recipients to delete them. There is also the possibility that the recipient will miss a relatively important message due to the relatively large number of unimportant messages accumulated in their email inbox. Such unsolicited bulk email messages are often known by the colloquial term “spam,” and senders of such messages are often known as “spammers.”
A first known method for detecting spam includes so-called “whitelists” and “blacklists,” in which the sender of each message is identified by the filter as known to be “good” (a sender who is not a spammer), or “bad” (a sender who is known to be a spammer). While these methods generally achieve the goal of filtering messages, they are subject to the drawback that the user is involved in managing the whitelist or blacklist, and the further drawback that spammers often choose new, unique, sending addresses from which to send new spam.
A second known method for detecting spam includes attempting to evaluate from the content of the message whether it is spam or not. Known evaluation techniques include (a) searching the message for known keywords that are typically indicative of spam, such as words identifying known products popularly promoted by spammers, and (b) evaluating the message by comparing the number of such “bad” keywords with probable “good” keywords, such as words relatively unlikely to be used in a spam message. One example of the latter method is the Bayesian filter proposed by Paul Graham, “A Plan for Spam,” and performed by some implementations of the “Mozilla” email client. While these methods generally achieve the goal of filtering messages, they are subject to the drawback that the user must train the implementation to recognize the “bad” keywords and “good” keywords particular to the type of message that user typically receives, and the further drawback that spammers often choose, new, unique, products to promote or words (often misspellings) with which to identify them.
Accordingly, it would be advantageous to provide an improved technique for dynamic message filtering.